Is Your Marketing Team Ready to Use AI?

Is Your Marketing Team Ready to Use AI?

Artificial Intelligence (AI) is evolving as a major disruptive force across industry sectors as it takes its place in the pantheon of enterprise software tools. Among AI's many advantages are the ability to target customers, reduce churn, and increase the effectiveness of advertising campaigns.

Data fuels the business insights that can be derived from statistical analysis using machine learning, a sub-field of AI in which machines learn from examples. But if you and your marketing team want to realize the benefits of AI, you need to start by aggregating the data about everything from target market analysis to insights about which media channels provide the highest ROI on your marketing dollar. Think of data as the food source for an AI machine that has an insatiable appetite.

Before you go too far down the rabbit hole into AI in Wonderland, you should assess how much data you have, its quality, and how easily can you access it. Here are five questions to consider as you prepare your data-driven marketing strategy:

  • Do you have a list of customers? This is one of the fundamental sets of data that you'll need before you can derive insights about your customers. Analysis of this data will help you better target your customers and prospective customers. You need be able to target your messaging.

  • Do you have a customer relationship management system (CRM) and can you leverage the data in it? CRMs are valuable sources of data about customers. A CRM tracks data — ranging from long-term company characteristics to the details of the latest contact with the customer — which is analyzed using AI to help build a fuller picture of your customers.

  • How do you track orders? This could be something as simple as a cash register to advanced order processing and fulfillment software. You don't need a full-blown enterprise processing system, but you do need a comprehensive data set of orders that can standardized for machine learning.

  • Do you have a way of accessing this data? If you are using a closed, proprietary system it may be a challenge to get data out of the system. The other challenge is the difficulty in getting access to data from multiple sources. Order processing systems should at least be able to export data to a common file format, such as comma separated values (CSVs).

  • What tools and data will you use to decide who your target market is? This may be obvious or it may be a more challenging task because you have multiple potential data sources. Consider the quality, quantity, and accessibility of data sets when choosing.

Get Ready for AI To Rock Your Marketing World

Once you can answer yes to all of these questions, you are ready to take advantage of machine learning. This information is the raw material which drive the process of building AI models. At this point, you should know who you want to market to and what you want to say. Now, it's time to prioritize your action steps. Here are four AI marketing-oriented objectives that can have a direct impact on the value of your campaigns.

  • Target marketing. In order to identify potential high-value customers that have not yet purchased or "good" customers ripe for repeat orders, you'll need data from both your CRM and order systems. It's not uncommon to collect and merge data from multiple systems when building AI models, which requires all data in a single database (unless you are able to aggregate data sources like Ki can). Focusing your AI efforts on target marketing will help make your campaigns more efficient and effective.

  • Reduce churn. Customer acquisition can be difficult and expensive. Once you land precious new customers, you don't want them leaving you for your competition. Using AI, you can develop models that can predict the likelihood that a customer will churn. To do this, you'll need data about customers and orders so the model can look for patterns that appear before a customer leaves. Depending on the kind of AI algorithm used, you can receive different kinds of information. When you use a classification model you can get a yes/no prediction of whether a customer is likely to churn. While you can infer the likelihood of a customers churn in that classification model, regression based models will explicitly return a customer's likelihood of return. In either case, AI has the potential to help you improve customer retention.

  • Coupon/discount strategy. It also is important to understand how customer pricing impacts order rates. You may find that some of your products are more price sensitive than others. In other cases, you could discover that some customer's purchase patterns are highly correlated with prices. In those cases, they may be good targets for coupons or discounts. Similarly, you may have some customers that are less sensitive to price increases and will likely continue to purchase without the added incentive of a discount. Using AI to develop a coupon/discount strategy is an effective way of maximizing sales revenue.

  • Bundling products. Can you increase sales by wrapping products? Your order data can help you identify products that are frequently bought together at the same time. You may find that customers that buy one product will frequently but a second product within two weeks. This kind of insight can help you build product bundles that streamline ordering for your customers and increases sales for your business.

AI has matured to the point that it is accessible to businesses of all sizes. But to really take advantage of AI, you need to be prepared. That means having have the raw data that AI algorithms need in order to turn your information into actionable business insights. Ultimately, AI can help marketers better target and retain customers — and that ultimately means increased sales revenue.

Getting started can be challenging but Keyence is experienced in data first strategies and data analysis and is ready to help you get your projects off the ground.

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